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Sequential
groups a linear stack of layers into a tf.keras.Model
.
tf.keras.Sequential( layers=None, name=None )
Sequential
provides training and inference features on this model.
# Optionally, the first layer can receive an `input_shape` argument: model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(8, input_shape=(16,))) # Afterwards, we do automatic shape inference: model.add(tf.keras.layers.Dense(4))
# This is identical to the following: model = tf.keras.Sequential() model.add(tf.keras.Input(shape=(16,))) model.add(tf.keras.layers.Dense(8))
# Note that you can also omit the `input_shape` argument. # In that case the model doesn't have any weights until the first call # to a training/evaluation method (since it isn't yet built): model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(8)) model.add(tf.keras.layers.Dense(4)) # model.weights not created yet
# Whereas if you specify the input shape, the model gets built # continuously as you are adding layers: model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(8, input_shape=(16,))) model.add(tf.keras.layers.Dense(4)) len(model.weights) 4
# When using the delayedbuild pattern (no input shape specified), you can # choose to manually build your model by calling # `build(batch_input_shape)`: model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(8)) model.add(tf.keras.layers.Dense(4)) model.build((None, 16)) len(model.weights) 4
# Note that when using the delayedbuild pattern (no input shape specified), # the model gets built the first time you call `fit`, `eval`, or `predict`, # or the first time you call the model on some input data. model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(8)) model.add(tf.keras.layers.Dense(1)) model.compile(optimizer='sgd', loss='mse') # This builds the model for the first time: model.fit(x, y, batch_size=32, epochs=10)
Args  

layers  Optional list of layers to add to the model. 
name  Optional name for the model. 
Attributes  

distribute_strategy  The tf.distribute.Strategy this model was created under. 
layers  
metrics_names  Returns the model's display labels for all outputs. Note: inputs = tf.keras.layers.Input(shape=(3,)) outputs = tf.keras.layers.Dense(2)(inputs) model = tf.keras.models.Model(inputs=inputs, outputs=outputs) model.compile(optimizer="Adam", loss="mse", metrics=["mae"]) model.metrics_names [] x = np.random.random((2, 3)) y = np.random.randint(0, 2, (2, 2)) model.fit(x, y) model.metrics_names ['loss', 'mae'] inputs = tf.keras.layers.Input(shape=(3,)) d = tf.keras.layers.Dense(2, name='out') output_1 = d(inputs) output_2 = d(inputs) model = tf.keras.models.Model( inputs=inputs, outputs=[output_1, output_2]) model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"]) model.fit(x, (y, y)) model.metrics_names ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae', 'out_1_acc'] 
run_eagerly  Settable attribute indicating whether the model should run eagerly. Running eagerly means that your model will be run step by step, like Python code. Your model might run slower, but it should become easier for you to debug it by stepping into individual layer calls. By default, we will attempt to compile your model to a static graph to deliver the best execution performance. 
add
add( layer )
Adds a layer instance on top of the layer stack.
Arguments  

layer  layer instance. 
Raises  

TypeError  If layer is not a layer instance. 
ValueError  In case the layer argument does not know its input shape. 
ValueError  In case the layer argument has multiple output tensors, or is already connected somewhere else (forbidden in Sequential models). 
compile
compile( optimizer='rmsprop', loss=None, metrics=None, loss_weights=None, weighted_metrics=None, run_eagerly=None, **kwargs )
Configures the model for training.
Arguments  

optimizer  String (name of optimizer) or optimizer instance. See tf.keras.optimizers . 
loss  String (name of objective function), objective function or tf.keras.losses.Loss instance. See tf.keras.losses . An objective function is any callable with the signature loss = fn(y_true, y_pred) , where y_true = ground truth values with shape = [batch_size, d0, .. dN] , except sparse loss functions such as sparse categorical crossentropy where shape = [batch_size, d0, .. dN1] . y_pred = predicted values with shape = [batch_size, d0, .. dN] . It returns a weighted loss float tensor. If a custom Loss instance is used and reduction is set to NONE, return value has the shape [batch_size, d0, .. dN1] ie. persample or pertimestep loss values; otherwise, it is a scalar. If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses. The loss value that will be minimized by the model will then be the sum of all individual losses. 
metrics  List of metrics to be evaluated by the model during training and testing. Each of this can be a string (name of a builtin function), function or a tf.keras.metrics.Metric instance. See tf.keras.metrics . Typically you will use metrics=['accuracy'] . A function is any callable with the signature result = fn(y_true, y_pred) . To specify different metrics for different outputs of a multioutput model, you could also pass a dictionary, such as metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']} . You can also pass a list (len = len(outputs)) of lists of metrics such as metrics=[['accuracy'], ['accuracy', 'mse']] or metrics=['accuracy', ['accuracy', 'mse']] . When you pass the strings 'accuracy' or 'acc', we convert this to one of tf.keras.metrics.BinaryAccuracy , tf.keras.metrics.CategoricalAccuracy , tf.keras.metrics.SparseCategoricalAccuracy based on the loss function used and the model output shape. We do a similar conversion for the strings 'crossentropy' and 'ce' as well. 
loss_weights  Optional list or dictionary specifying scalar coefficients (Python floats) to weight the loss contributions of different model outputs. The loss value that will be minimized by the model will then be the weighted sum of all individual losses, weighted by the loss_weights coefficients. If a list, it is expected to have a 1:1 mapping to the model's outputs. If a dict, it is expected to map output names (strings) to scalar coefficients. 
weighted_metrics  List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing. 
run_eagerly  Bool. Defaults to False . If True , this Model 's logic will not be wrapped in a tf.function . Recommended to leave this as None unless your Model cannot be run inside a tf.function . 
**kwargs  Any additional arguments. Supported arguments:

Raises  

ValueError  In case of invalid arguments for optimizer , loss or metrics . 
evaluate
evaluate( x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, return_dict=False )
Returns the loss value & metrics values for the model in test mode.
Computation is done in batches (see the batch_size
arg.)
Arguments  

x  Input data. It could be:

y  Target data. Like the input data x , it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely). If x is a dataset, generator or keras.utils.Sequence instance, y should not be specified (since targets will be obtained from the iterator/dataset). 
batch_size  Integer or None . Number of samples per batch of computation. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of a dataset, generators, or keras.utils.Sequence instances (since they generate batches). 
verbose  0 or 1. Verbosity mode. 0 = silent, 1 = progress bar. 
sample_weight  Optional Numpy array of weights for the test samples, used for weighting the loss function. You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape (samples, sequence_length) , to apply a different weight to every timestep of every sample. This argument is not supported when x is a dataset, instead pass sample weights as the third element of x . 
steps  Integer or None . Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of None . If x is a tf.data dataset and steps is None, 'evaluate' will run until the dataset is exhausted. This argument is not supported with array inputs. 
callbacks  List of keras.callbacks.Callback instances. List of callbacks to apply during evaluation. See callbacks. 
max_queue_size  Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10. 
workers  Integer. Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using processbased threading. If unspecified, workers will default to 1. If 0, will execute the generator on the main thread. 
use_multiprocessing  Boolean. Used for generator or keras.utils.Sequence input only. If True , use processbased threading. If unspecified, use_multiprocessing will default to False . Note that because this implementation relies on multiprocessing, you should not pass nonpicklable arguments to the generator as they can't be passed easily to children processes. 
return_dict  If True , loss and metric results are returned as a dict, with each key being the name of the metric. If False , they are returned as a list. 
See the discussion of Unpacking behavior for iteratorlike inputs
for Model.fit
.
Returns  

Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs. 
Raises  

RuntimeError  If model.evaluate is wrapped in tf.function . 
ValueError  in case of invalid arguments. 
evaluate_generator
evaluate_generator( generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0 )
Evaluates the model on a data generator. (deprecated)
Model.evaluate
now supports generators, so there is no longer any need to use this endpoint.
fit
fit( x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_batch_size=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False )
Trains the model for a fixed number of epochs (iterations on a dataset).
Arguments  

x  Input data. It could be:

y  Target data. Like the input data x , it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely). If x is a dataset, generator, or keras.utils.Sequence instance, y should not be specified (since targets will be obtained from x ). 
batch_size  Integer or None . Number of samples per gradient update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of datasets, generators, or keras.utils.Sequence instances (since they generate batches). 
epochs  Integer. Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided. Note that in conjunction with initial_epoch , epochs is to be understood as "final epoch". The model is not trained for a number of iterations given by epochs , but merely until the epoch of index epochs is reached. 
verbose  0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (eg, in a production environment). 
callbacks  List of keras.callbacks.Callback instances. List of callbacks to apply during training. See tf.keras.callbacks . 
validation_split  Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the x and y data provided, before shuffling. This argument is not supported when x is a dataset, generator or keras.utils.Sequence instance. 
validation_data  Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. Thus, note the fact that the validation loss of data provided using validation_split or validation_data is not affected by regularization layers like noise and dropuout. validation_data will override validation_split . validation_data could be: (x_val, y_val) of Numpy arrays or tensors(x_val, y_val, val_sample_weights) of Numpy arraysbatch_size must be provided. For the last case, validation_steps could be provided. Note that validation_data does not support all the data types that are supported in x , eg, dict, generator or keras.utils.Sequence . 
shuffle  Boolean (whether to shuffle the training data before each epoch) or str (for 'batch'). This argument is ignored when x is a generator. 'batch' is a special option for dealing with the limitations of HDF5 data; it shuffles in batchsized chunks. Has no effect when steps_per_epoch is not None . 
class_weight  Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an underrepresented class. 
sample_weight  Optional Numpy array of weights for the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape (samples, sequence_length) , to apply a different weight to every timestep of every sample. This argument is not supported when x is a dataset, generator, or keras.utils.Sequence instance, instead provide the sample_weights as the third element of x . 
initial_epoch  Integer. Epoch at which to start training (useful for resuming a previous training run). 
steps_per_epoch  Integer or None . Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with input tensors such as TensorFlow data tensors, the default None is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a tf.data dataset, and 'steps_per_epoch' is None, the epoch will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the steps_per_epoch argument. This argument is not supported with array inputs. 
validation_steps  Only relevant if validation_data is provided and is a tf.data dataset. Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch. If 'validation_steps' is None, validation will run until the validation_data dataset is exhausted. In the case of an infinitely repeated dataset, it will run into an infinite loop. If 'validation_steps' is specified and only part of the dataset will be consumed, the evaluation will start from the beginning of the dataset at each epoch. This ensures that the same validation samples are used every time. 
validation_batch_size  Integer or None . Number of samples per validation batch. If unspecified, will default to batch_size . Do not specify the validation_batch_size if your data is in the form of datasets, generators, or keras.utils.Sequence instances (since they generate batches). 
validation_freq  Only relevant if validation data is provided. Integer or collections_abc.Container instance (e.g. list, tuple, etc.). If an integer, specifies how many training epochs to run before a new validation run is performed, e.g. validation_freq=2 runs validation every 2 epochs. If a Container, specifies the epochs on which to run validation, e.g. validation_freq=[1, 2, 10] runs validation at the end of the 1st, 2nd, and 10th epochs. 
max_queue_size  Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10. 
workers  Integer. Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using processbased threading. If unspecified, workers will default to 1. If 0, will execute the generator on the main thread. 
use_multiprocessing  Boolean. Used for generator or keras.utils.Sequence input only. If True , use processbased threading. If unspecified, use_multiprocessing will default to False . Note that because this implementation relies on multiprocessing, you should not pass nonpicklable arguments to the generator as they can't be passed easily to children processes. 
Unpacking behavior for iteratorlike inputs: A common pattern is to pass a tf.data.Dataset, generator, or tf.keras.utils.Sequence to the x
argument of fit, which will in fact yield not only features (x) but optionally targets (y) and sample weights. Keras requires that the output of such iteratorlikes be unambiguous. The iterator should return a tuple of length 1, 2, or 3, where the optional second and third elements will be used for y and sample_weight respectively. Any other type provided will be wrapped in a length one tuple, effectively treating everything as 'x'. When yielding dicts, they should still adhere to the toplevel tuple structure. e.g. ({"x0": x0, "x1": x1}, y)
. Keras will not attempt to separate features, targets, and weights from the keys of a single dict. A notable unsupported data type is the namedtuple. The reason is that it behaves like both an ordered datatype (tuple) and a mapping datatype (dict). So given a namedtuple of the form: namedtuple("example_tuple", ["y", "x"])
it is ambiguous whether to reverse the order of the elements when interpreting the value. Even worse is a tuple of the form: namedtuple("other_tuple", ["x", "y", "z"])
where it is unclear if the tuple was intended to be unpacked into x, y, and sample_weight or passed through as a single element to x
. As a result the data processing code will simply raise a ValueError if it encounters a namedtuple. (Along with instructions to remedy the issue.)
Returns  

A History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable). 
Raises  

RuntimeError 

ValueError  In case of mismatch between the provided input data and what the model expects. 
fit_generator
fit_generator( generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, validation_freq=1, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0 )
Fits the model on data yielded batchbybatch by a Python generator. (deprecated)
Model.fit
now supports generators, so there is no longer any need to use this endpoint.
get_layer
get_layer( name=None, index=None )
Retrieves a layer based on either its name (unique) or index.
If name
and index
are both provided, index
will take precedence. Indices are based on order of horizontal graph traversal (bottomup).
Arguments  

name  String, name of layer. 
index  Integer, index of layer. 
Returns  

A layer instance. 
Raises  

ValueError  In case of invalid layer name or index. 
load_weights
load_weights( filepath, by_name=False, skip_mismatch=False, options=None )
Loads all layer weights, either from a TensorFlow or an HDF5 weight file.
If by_name
is False weights are loaded based on the network's topology. This means the architecture should be the same as when the weights were saved. Note that layers that don't have weights are not taken into account in the topological ordering, so adding or removing layers is fine as long as they don't have weights.
If by_name
is True, weights are loaded into layers only if they share the same name. This is useful for finetuning or transferlearning models where some of the layers have changed.
Only topological loading (by_name=False
) is supported when loading weights from the TensorFlow format. Note that topological loading differs slightly between TensorFlow and HDF5 formats for userdefined classes inheriting from tf.keras.Model
: HDF5 loads based on a flattened list of weights, while the TensorFlow format loads based on the objectlocal names of attributes to which layers are assigned in the Model
's constructor.
Arguments  

filepath  String, path to the weights file to load. For weight files in TensorFlow format, this is the file prefix (the same as was passed to save_weights ). 
by_name  Boolean, whether to load weights by name or by topological order. Only topological loading is supported for weight files in TensorFlow format. 
skip_mismatch  Boolean, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weight (only valid when by_name=True ). 
options  Optional tf.train.CheckpointOptions object that specifies options for loading weights. 
Returns  

When loading a weight file in TensorFlow format, returns the same status object as tf.train.Checkpoint.restore . When graph building, restore ops are run automatically as soon as the network is built (on first call for userdefined classes inheriting from Model , immediately if it is already built). When loading weights in HDF5 format, returns 
Raises  

ImportError  If h5py is not available and the weight file is in HDF5 format. 
ValueError  If skip_mismatch is set to True when by_name is False . 
make_predict_function
make_predict_function()
Creates a function that executes one step of inference.
This method can be overridden to support custom inference logic. This method is called by Model.predict
and Model.predict_on_batch
.
Typically, this method directly controls tf.function
and tf.distribute.Strategy
settings, and delegates the actual evaluation logic to Model.predict_step
.
This function is cached the first time Model.predict
or Model.predict_on_batch
is called. The cache is cleared whenever Model.compile
is called.
Returns  

Function. The function created by this method should accept a tf.data.Iterator , and return the outputs of the Model . 
make_test_function
make_test_function()
Creates a function that executes one step of evaluation.
This method can be overridden to support custom evaluation logic. This method is called by Model.evaluate
and Model.test_on_batch
.
Typically, this method directly controls tf.function
and tf.distribute.Strategy
settings, and delegates the actual evaluation logic to Model.test_step
.
This function is cached the first time Model.evaluate
or Model.test_on_batch
is called. The cache is cleared whenever Model.compile
is called.
Returns  

Function. The function created by this method should accept a tf.data.Iterator , and return a dict containing values that will be passed to tf.keras.Callbacks.on_test_batch_end . 
make_train_function
make_train_function()
Creates a function that executes one step of training.
This method can be overridden to support custom training logic. This method is called by Model.fit
and Model.train_on_batch
.
Typically, this method directly controls tf.function
and tf.distribute.Strategy
settings, and delegates the actual training logic to Model.train_step
.
This function is cached the first time Model.fit
or Model.train_on_batch
is called. The cache is cleared whenever Model.compile
is called.
Returns  

Function. The function created by this method should accept a tf.data.Iterator , and return a dict containing values that will be passed to tf.keras.Callbacks.on_train_batch_end , such as {'loss': 0.2, 'accuracy': 0.7} . 
pop
pop()
Removes the last layer in the model.
Raises  

TypeError  if there are no layers in the model. 
predict
predict( x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False )
Generates output predictions for the input samples.
Computation is done in batches. This method is designed for performance in large scale inputs. For small amount of inputs that fit in one batch, directly using __call__
is recommended for faster execution, e.g., model(x)
, or model(x, training=False)
if you have layers such as tf.keras.layers.BatchNormalization
that behaves differently during inference. Also, note the fact that test loss is not affected by regularization layers like noise and dropout.
Arguments  

x  Input samples. It could be:

batch_size  Integer or None . Number of samples per batch. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of dataset, generators, or keras.utils.Sequence instances (since they generate batches). 
verbose  Verbosity mode, 0 or 1. 
steps  Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of None . If x is a tf.data dataset and steps is None, predict will run until the input dataset is exhausted. 
callbacks  List of keras.callbacks.Callback instances. List of callbacks to apply during prediction. See callbacks. 
max_queue_size  Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10. 
workers  Integer. Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using processbased threading. If unspecified, workers will default to 1. If 0, will execute the generator on the main thread. 
use_multiprocessing  Boolean. Used for generator or keras.utils.Sequence input only. If True , use processbased threading. If unspecified, use_multiprocessing will default to False . Note that because this implementation relies on multiprocessing, you should not pass nonpicklable arguments to the generator as they can't be passed easily to children processes. 
See the discussion of Unpacking behavior for iteratorlike inputs
for Model.fit
. Note that Model.predict uses the same interpretation rules as Model.fit
and Model.evaluate
, so inputs must be unambiguous for all three methods.
Returns  

Numpy array(s) of predictions. 
Raises  

RuntimeError  If model.predict is wrapped in tf.function . 
ValueError  In case of mismatch between the provided input data and the model's expectations, or in case a stateful model receives a number of samples that is not a multiple of the batch size. 
predict_classes
predict_classes( x, batch_size=32, verbose=0 )
Generate class predictions for the input samples. (deprecated)
The input samples are processed batch by batch.
Arguments  

x  input data, as a Numpy array or list of Numpy arrays (if the model has multiple inputs). 
batch_size  integer. 
verbose  verbosity mode, 0 or 1. 
Returns  

A numpy array of class predictions. 
predict_generator
predict_generator( generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0 )
Generates predictions for the input samples from a data generator. (deprecated)
Model.predict
now supports generators, so there is no longer any need to use this endpoint.
predict_on_batch
predict_on_batch( x )
Returns predictions for a single batch of samples.
Arguments  

x  Input data. It could be:  A Numpy array (or arraylike), or a list of arrays (in case the model has multiple inputs).  A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). 
Returns  

Numpy array(s) of predictions. 
Raises  

RuntimeError  If model.predict_on_batch is wrapped in tf.function . 
ValueError  In case of mismatch between given number of inputs and expectations of the model. 
predict_proba
predict_proba( x, batch_size=32, verbose=0 )
Generates class probability predictions for the input samples. (deprecated)
The input samples are processed batch by batch.
Arguments  

x  input data, as a Numpy array or list of Numpy arrays (if the model has multiple inputs). 
batch_size  integer. 
verbose  verbosity mode, 0 or 1. 
Returns  

A Numpy array of probability predictions. 
predict_step
predict_step( data )
The logic for one inference step.
This method can be overridden to support custom inference logic. This method is called by Model.make_predict_function
.
This method should contain the mathemetical logic for one step of inference. This typically includes the forward pass.
Configuration details for how this logic is run (e.g. tf.function
and tf.distribute.Strategy
settings), should be left to Model.make_predict_function
, which can also be overridden.
Arguments  

data  A nested structure of Tensor s. 
Returns  

The result of one inference step, typically the output of calling the Model on data. 
reset_metrics
reset_metrics()
Resets the state of all the metrics in the model.
inputs = tf.keras.layers.Input(shape=(3,)) outputs = tf.keras.layers.Dense(2)(inputs) model = tf.keras.models.Model(inputs=inputs, outputs=outputs) model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
x = np.random.random((2, 3)) y = np.random.randint(0, 2, (2, 2)) _ = model.fit(x, y, verbose=0) assert all(float(m.result()) for m in model.metrics)
model.reset_metrics() assert all(float(m.result()) == 0 for m in model.metrics)
reset_states
reset_states()
save
save( filepath, overwrite=True, include_optimizer=True, save_format=None, signatures=None, options=None )
Saves the model to Tensorflow SavedModel or a single HDF5 file.
This allows you to save the entirety of the state of a model in a single file.
Saved models can be reinstantiated via keras.models.load_model
. The model returned by load_model
is a compiled model ready to be used (unless the saved model was never compiled in the first place).
Models built with the Sequential and Functional API can be saved to both the HDF5 and SavedModel formats. Subclassed models can only be saved with the SavedModel format.
Note that the model weights may have different scoped names after being loaded. Scoped names include the model/layer names, such as "dense_1/kernel:0"
. It is recommended that you use the layer properties to access specific variables, e.g. model.get_layer("dense_1").kernel
.
Arguments  

filepath  String, PathLike, path to SavedModel or H5 file to save the model. 
overwrite  Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt. 
include_optimizer  If True, save optimizer's state together. 
save_format  Either 'tf' or 'h5' , indicating whether to save the model to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X, and 'h5' in TF 1.X. 
signatures  Signatures to save with the SavedModel. Applicable to the 'tf' format only. Please see the signatures argument in tf.saved_model.save for details. 
options  Optional tf.saved_model.SaveOptions object that specifies options for saving to SavedModel. 
from keras.models import load_model model.save('my_model.h5') # creates a HDF5 file 'my_model.h5' del model # deletes the existing model # returns a compiled model # identical to the previous one model = load_model('my_model.h5')
save_weights
save_weights( filepath, overwrite=True, save_format=None, options=None )
Saves all layer weights.
Either saves in HDF5 or in TensorFlow format based on the save_format
argument.
When saving in HDF5 format, the weight file has:
layer_names
(attribute), a list of strings (ordered names of model layers).group
named layer.name
weight_names
, a list of strings (ordered names of weights tensor of the layer).When saving in TensorFlow format, all objects referenced by the network are saved in the same format as tf.train.Checkpoint
, including any Layer
instances or Optimizer
instances assigned to object attributes. For networks constructed from inputs and outputs using tf.keras.Model(inputs, outputs)
, Layer
instances used by the network are tracked/saved automatically. For userdefined classes which inherit from tf.keras.Model
, Layer
instances must be assigned to object attributes, typically in the constructor. See the documentation of tf.train.Checkpoint
and tf.keras.Model
for details.
While the formats are the same, do not mix save_weights
and tf.train.Checkpoint
. Checkpoints saved by Model.save_weights
should be loaded using Model.load_weights
. Checkpoints saved using tf.train.Checkpoint.save
should be restored using the corresponding tf.train.Checkpoint.restore
. Prefer tf.train.Checkpoint
over save_weights
for training checkpoints.
The TensorFlow format matches objects and variables by starting at a root object, self
for save_weights
, and greedily matching attribute names. For Model.save
this is the Model
, and for Checkpoint.save
this is the Checkpoint
even if the Checkpoint
has a model attached. This means saving a tf.keras.Model
using save_weights
and loading into a tf.train.Checkpoint
with a Model
attached (or vice versa) will not match the Model
's variables. See the guide to training checkpoints for details on the TensorFlow format.
Arguments  

filepath  String or PathLike, path to the file to save the weights to. When saving in TensorFlow format, this is the prefix used for checkpoint files (multiple files are generated). Note that the '.h5' suffix causes weights to be saved in HDF5 format. 
overwrite  Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt. 
save_format  Either 'tf' or 'h5'. A filepath ending in '.h5' or '.keras' will default to HDF5 if save_format is None . Otherwise None defaults to 'tf'. 
options  Optional tf.train.CheckpointOptions object that specifies options for saving weights. 
Raises  

ImportError  If h5py is not available when attempting to save in HDF5 format. 
ValueError  For invalid/unknown format arguments. 
summary
summary( line_length=None, positions=None, print_fn=None )
Prints a string summary of the network.
Arguments  

line_length  Total length of printed lines (e.g. set this to adapt the display to different terminal window sizes). 
positions  Relative or absolute positions of log elements in each line. If not provided, defaults to [.33, .55, .67, 1.] . 
print_fn  Print function to use. Defaults to print . It will be called on each line of the summary. You can set it to a custom function in order to capture the string summary. 
Raises  

ValueError  if summary() is called before the model is built. 
test_on_batch
test_on_batch( x, y=None, sample_weight=None, reset_metrics=True, return_dict=False )
Test the model on a single batch of samples.
Arguments  

x  Input data. It could be:  A Numpy array (or arraylike), or a list of arrays (in case the model has multiple inputs).  A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).

y  Target data. Like the input data x , it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely). 
sample_weight  Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. 
reset_metrics  If True , the metrics returned will be only for this batch. If False , the metrics will be statefully accumulated across batches. 
return_dict  If True , loss and metric results are returned as a dict, with each key being the name of the metric. If False , they are returned as a list. 
Returns  

Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs. 
Raises  

RuntimeError  If model.test_on_batch is wrapped in tf.function . 
ValueError  In case of invalid userprovided arguments. 
test_step
test_step( data )
The logic for one evaluation step.
This method can be overridden to support custom evaluation logic. This method is called by Model.make_test_function
.
This function should contain the mathemetical logic for one step of evaluation. This typically includes the forward pass, loss calculation, and metrics updates.
Configuration details for how this logic is run (e.g. tf.function
and tf.distribute.Strategy
settings), should be left to Model.make_test_function
, which can also be overridden.
Arguments  

data  A nested structure of Tensor s. 
Returns  

A dict containing values that will be passed to tf.keras.callbacks.CallbackList.on_train_batch_end . Typically, the values of the Model 's metrics are returned. 
to_json
to_json( **kwargs )
Returns a JSON string containing the network configuration.
To load a network from a JSON save file, use keras.models.model_from_json(json_string, custom_objects={})
.
Arguments  

**kwargs  Additional keyword arguments to be passed to json.dumps() . 
Returns  

A JSON string. 
to_yaml
to_yaml( **kwargs )
Returns a yaml string containing the network configuration.
To load a network from a yaml save file, use keras.models.model_from_yaml(yaml_string, custom_objects={})
.
custom_objects
should be a dictionary mapping the names of custom losses / layers / etc to the corresponding functions / classes.
Arguments  

**kwargs  Additional keyword arguments to be passed to yaml.dump() . 
Returns  

A YAML string. 
Raises  

ImportError  if yaml module is not found. 
train_on_batch
train_on_batch( x, y=None, sample_weight=None, class_weight=None, reset_metrics=True, return_dict=False )
Runs a single gradient update on a single batch of data.
Arguments  

x  Input data. It could be:

y  Target data. Like the input data x , it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely). 
sample_weight  Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. 
class_weight  Optional dictionary mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an underrepresented class. 
reset_metrics  If True , the metrics returned will be only for this batch. If False , the metrics will be statefully accumulated across batches. 
return_dict  If True , loss and metric results are returned as a dict, with each key being the name of the metric. If False , they are returned as a list. 
Returns  

Scalar training loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs. 
Raises  

RuntimeError  If model.train_on_batch is wrapped in tf.function . 
ValueError  In case of invalid userprovided arguments. 
train_step
train_step( data )
The logic for one training step.
This method can be overridden to support custom training logic. This method is called by Model.make_train_function
.
This method should contain the mathemetical logic for one step of training. This typically includes the forward pass, loss calculation, backpropagation, and metric updates.
Configuration details for how this logic is run (e.g. tf.function
and tf.distribute.Strategy
settings), should be left to Model.make_train_function
, which can also be overridden.
Arguments  

data  A nested structure of Tensor s. 
Returns  

A dict containing values that will be passed to tf.keras.callbacks.CallbackList.on_train_batch_end . Typically, the values of the Model 's metrics are returned. Example: {'loss': 0.2, 'accuracy': 0.7} . 
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/Sequential